Attention Coding for Latent Tensors With Smaller Probability Models
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Solution Overview
Problem
Existing 3D Gaussian splatting techniques face challenges in model size due to the need for a massive number of parameters and data points, leading to high storage requirements, despite achieving high-quality and fast rendering.
Innovation Solution
Replace latent feature vectors with small query vectors and use a scaled-dot-product-attention mechanism with separate sets of key and value vectors to reduce redundancy and model size, while maintaining visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D Gaussian splatting with latent feature vectors is used, then visual quality is maintained, but model size becomes excessively large
Solution Approach 1:
The patent extracts and removes the redundant latent feature vectors from the 3D Gaussian splatting representation. By identifying that these feature vectors are not essential for visual reconstruction, the patent eliminates them entirely, keeping only the minimal necessary parameters (position, covariance, opacity, and color) that directly contribute to visual quality while dramatically reducing model size by 4×.
Solution Approach 2:
The patent creates a simplified copy of the traditional Gaussian splatting representation by replacing complex latent feature vectors with direct parameter storage. Instead of storing high-dimensional latent features that require decomposition, the patent stores the actual reconstructed parameters that can be directly used for rendering, achieving both compression and visual fidelity.
2Measurement precision
If more parameters and data points are used in 3D Gaussian splatting, then rendering quality improves, but storage requirements increase
Solution Approach 1:
The patent adopts a disposable approach to parameter storage by keeping only the essential, irreducible parameters needed for rendering (position, covariance, opacity, color) and discarding all redundant information. This minimal parameter set acts as a 'cheap' representation that can be easily stored and transmitted while still enabling high-quality rendering when needed.
Solution Approach 2:
The patent fundamentally changes the parameter representation from high-dimensional latent feature vectors to low-dimensional direct rendering parameters. This parameter transformation reduces the dimensionalality from potentially hundreds of features per Gaussian to just 4-5 essential parameters, achieving compression while maintaining the ability to reconstruct high-quality visuals.
3Loss of information
If latent feature vectors are stored for each Gaussian, then comprehensive information is retained, but redundancy increases
Solution Approach 1:
The patent extracts and removes the redundant latent feature vectors from the Gaussian representation. By analyzing what information is actually necessary for visual reconstruction, the patent identifies and eliminates the redundant feature vectors, keeping only the essential parameters that directly contribute to rendering quality without duplication.
Solution Approach 2:
Instead of storing comprehensive latent features and decompressing them during rendering, the patent inverts the approach by storing only the final essential parameters directly in their uncompressed, ready-to-use form. This inversion eliminates the need for decomposition operations and removes all redundancy associated with latent feature storage.
Data Source
AI summary
Methods and apparatuses are described for entropy encoding and decoding of a latent tensor, which includes separating the latent tensor into segments in the spatial dimensions and in the channel dimension, each segment including at least one latent tensor element. An arrangement of the segments is processed by a neural network: the neural network includes at least one attention layer. Based on the processed segment a probability model is obtained for entropy encoding or decoding of a latent tensor element.